Application of Ultrahigh-Performance Liquid Chromatography and Electrospray Ionization Quadrupole Orbitrap High-Resolution Mass Spectrometry for Determination of 166 Pesticides in Fruits and Vegetables
Bibliographic record
Abstract
This paper presents an application of ultrahigh-performance liquid chromatography and electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC/ESI Q-Orbitrap) for determination of 166 pesticide residues in fruits and vegetables. Pesticides were extracted from the samples using the QuEChERS (quick, easy, cheap, effective, rugged, and safe) procedure. UHPLC/ESI Q-Orbitrap MS (i.e., full MS scan) acquired full MS data for quantification, and UHPLC/ESI Q-Orbitrap dd-MS(2) (i.e., data-dependent scan) obtained product-ion spectra for confirmation. UHPLC/ESI Q-Orbitrap MS quantification was achieved using matrix-matched standard calibration curves with isotopically labeled standards or chemical analogues as internal standards. The method performance characteristics that included overall recovery, intermediate precision, and measurement uncertainty were evaluated according to a nested experimental design. For the matrices studied, about 90.3-91.5% of the pesticides had recoveries between 81 and 110%, 92.1-97.6% had intermediate precision ≤20%, and 89.7-95.2% had measurement uncertainty ≤40%. Confirmation was based on mass accuracy ≤5 ppm and LC retention time tolerance within ±2.5%. Overall, the UHPLC/ESI Q-Orbitrap has demonstrated great performance for quantification and confirmation of pesticide residues in fresh fruits and vegetables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".